Accelerating network-on-chip simulation via sampling
Bibliographic record
Abstract
Architectural complexity continues to grow as we consider the large design space of multiple cores, cache architectures, networks-on-chip and memory controllers for emerging architectures. Simulators are growing in complexity to reflect each of these system components. However, many full-system simulators fail to take advantage of the underlying hardware resources such as multiple cores; as a result, simulation times have grown significantly in recent years. Long turnaround times limit the range and depth of design space exploration that is tractable. Communication has emerged as a first class design consideration and has led to significant research into networks-on-chip (NoC). The NoC is yet another component of the architecture that must be faithfully modeled in simulation. Given its importance, we focus on accelerating NoC simulation through the use of sampling techniques; sampling can provide both accurate results and fast evaluation. We propose NoCLabs and NoCPoint, two sampling methodologies utilizing statistical sampling theory and traffic phase behavior, respectively. Experimental results show that our proposed NoCLabs and NoCPoint estimate NoC performance with an average error of 5% while achieving one order of magnitude speedup on average.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".